Evaluation of a Data Assimilation System for Land Surface Models Using CLM4.5. (21st October 2018)

• Record Type: Journal Article
• Authors: Fox, Andrew M.; Hoar, Timothy J.; Anderson, Jeffrey L.; Arellano, Avelino F.; Smith, William K.; Litvak, Marcy E.; MacBean, Natasha; Schimel, David S.; Moore, David J. P.
• Is Part Of: Journal of advances in modeling earth systems. Volume 10:Number 10(2018)
• Page Start: 2471
• Page End: 2494
• Publication Date: 2018-10-21
• Subjects: community land model -- data assimilation research testbed -- carbon cycle -- data assimilation -- remote sensing; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2018MS001362
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: The magnitude and persistence of land carbon (C) pools influence long‐term climate feedbacks. Interactive ecological processes influence land C pools and our understanding of these processes is imperfect so land surface models have errors and biases when compared to each other and to real observations. Here we implement an Ensemble Adjustment Kalman Filter (EAKF), a sequential state data assimilation technique to reduce these errors and biases. We implement the EAKF using the Data Assimilation Research Testbed coupled with the Community Land Model (CLM 4.5 in CESM 1.2). We assimilated simulated and real satellite observations for a site in central New Mexico, United States. A series of observing system simulation experiments allowed assessment of the data assimilation system without model error. This showed that assimilating biomass and leaf area index observations decreased model error in C dynamics forecasts (29% using biomass observations and 40% using leaf area index observations) and that assimilation in combination shows greater improvement (51% using both observation streams). Assimilating real observations highlighted likely model structural errors and we implemented an adaptive model‐variance‐inflation technique to allow the model to track the observations. Monthly and longer model forecasts using real observations were improved relative to forecasts without data assimilation. The reliable forecast lead‐time varied by model pool and is dependent on how tightly the C pool is coupled to meteorologically driven processes. The EAKF and similar state data assimilation techniques could reduce errors in projections of the land C sink and provide more robust forecasts of C pools and land‐atmosphere exchanges. Plain Language Summary: The amount of carbon stored in vegetation and soils is an important control on how much carbon dioxide is in the atmosphere, and that influences future climate. Land surface models are used to simulate where this carbon is, but they are imperfect and there are often differences between model predictions and observations of the carbon stores. Here we describe a system that combines model predictions and observations and updates the modeled carbon stores so they are closer to the observations, considering uncertainty in both the model and the observations. We test our system at a location in New Mexico, United States, where we use observations from satellites of the amount of leaves on the vegetation and the amount of carbon stored in the vegetation. When we combine these observations with our land surface model there are large changes in the predicted amounts of stored carbon and the times of the year when the vegetation has the most leaves. These changes persist in the model after we stop updating it with observations, improving the model forecast. Key Points: Data assimilation was used to initialize biomass and leaf area in the Community Land Model Adaptive inflation was needed to give more weight to observations due to substantial discrepancies between model forecast and observations Data assimilation reduces forecast error in a land surface model